A construction worker safety management and control method and system for smart safety helmets

Abstract

The present invention discloses a construction worker safety management and control method and system for smart hard hats. The method includes collecting motion data and geographic location information of target construction workers, identifying the construction area and type of work in which the target construction worker is located, analyzing the correlation between the motion data and the target construction worker's location and determining whether a preset threshold has been reached. If the physiological status data of the target construction worker is not collected through the smart hard hat, the target construction worker's health index is assessed. If the target construction worker is outside the safe range, an assistance instruction is sent to other construction workers closest to the target construction worker, and path guidance information is generated to prompt the target construction worker to go to the area where the target construction worker is located. If the target construction worker is within the construction safety range, historical work data is obtained to match the target construction worker with a new construction area, and the target construction worker is dispatched to the new construction area. The present invention can accurately monitor the working status and location information of construction workers, and provide alarms and assistance instructions when risks are identified, thereby reducing the possibility of accidents and improving construction safety.

Description

Technical Field

[0001] The present invention relates to the technical field of construction safety monitoring, and in particular to a construction worker safety management and control method and system for smart safety helmets. Background Art

[0002] In traditional construction safety management, the safety monitoring of construction workers mainly relies on manual inspections, fixed camera monitoring, and simple personal protective equipment such as ordinary safety helmets. However, these methods have many limitations. First, manual inspections cannot achieve real-time and comprehensive coverage of the construction site, making it difficult to detect and respond to potential safety hazards in a timely manner. Secondly, the monitoring range of fixed cameras is limited, especially in large or complex structure construction sites, where there may be blind spots for monitoring, and it is impossible to ensure that all construction areas are within the monitoring range. In addition, traditional personal protective equipment lacks intelligent functions, cannot actively monitor the wearer's health status and work behavior, and cannot assess the safety status of the area where the construction workers themselves are located, making it difficult to provide early warnings before accidents occur. Summary of the Invention

[0003] In order to solve at least one of the technical problems mentioned above, the present invention provides a construction worker safety management method and system for smart safety helmets.

[0004] In a first aspect, the present invention provides a construction worker safety management method for a smart hard hat, the method comprising:

[0005] Collect the target construction worker's motion data and geographic location information, identify the target construction worker's construction area and corresponding work type based on the geographic location information, and analyze the correlation between the motion data and the work type;

[0006] Determine whether the correlation between the motion data and the type of work has reached a preset threshold; if the correlation has not reached the preset threshold, collect the physiological status data of the target construction worker through the smart safety helmet, and evaluate the current health index of the target construction worker based on the physiological status data;

[0007] If the health index is outside the safety range required for construction, an alarm is generated and sent to the monitoring platform, so that the monitoring platform sends an assistance instruction to at least one other construction worker closest to the target construction worker and generates a route guidance message to prompt at least one other construction worker to go to the area where the target construction worker is located;

[0008] If the health index is within the safety range required by the construction, the historical work data of the target construction personnel is obtained, a new construction area is matched for the target construction personnel based on the historical work data, and the target construction personnel is dispatched to the new construction area.

[0009] In one embodiment, collecting the geographic location information of the target construction personnel includes:

[0010] Calculate the The received signal of the path:

[0011] ;

[0012] Where, Indicates the The received signal of the path, Indicates the The amplitude of the received signal on each path, is the imaginary unit, is the carrier frequency, It is The initial phase of each path, Indicates time;

[0013] Determine the The key parameters of each path include amplitude attenuation factor and phase offset:

[0014] ;

[0015] ;

[0016] Where, Indicates the path The amplitude attenuation factor, Indicates the path The geometric distance, Represents the path loss index, satisfying ; Indicates the path The phase offset, is a direct path signal, which represents the shortest path between the transmitting source and the receiving point;

[0017] Determine the first The path quality weight of each path:

[0018] ;

[0019] ;

[0020] ;

[0021] Where, For the The path quality weight of each path, are the path reliability indices before and after normalization, are the minimum and maximum values ​​of the path reliability index, is the adjustment coefficient, satisfying ;

[0022] Get the initial position information collected by the sensor , correct the initial location information and obtain the final geographic location information of the target construction personnel :

[0023] ;

[0024] ;

[0025] Where, Indicates signal transmission sharing Path, Indicates that the signal The propagation speed along the path.

[0026] In one embodiment, analyzing the correlation between the action data and the job type includes:

[0027] Assign work types to construction areas and extract features from motion data, constructing feature vectors based on average acceleration, standard deviation, frequency distribution, and angular velocity change rate;

[0028] The dependency relationship between the job type and each eigenvector is established, and the posterior probability under the given job type condition is calculated through the Bayesian network model. The correlation degree between the action data and the job type is analyzed according to the size of the posterior probability value.

[0029] In one embodiment, the method further comprises:

[0030] The target construction personnel are identified based on their geographic location information to determine whether they are in a dangerous area or a non-construction area. If so, an alarm is generated to prompt the target construction personnel to move to a safe area or a construction area.

[0031] In one embodiment, the physiological status data includes heart rate, heart rate variability, blood pressure, body temperature, and blood oxygen saturation.

[0032] In a second aspect, the present invention further provides a construction worker safety management and control system for a smart hard hat, the system comprising:

[0033] A data collection unit is used to collect the motion data and geographic location information of the target construction personnel, identify the construction area where the target construction personnel are located and the corresponding work type based on the geographic location information, and analyze the correlation between the motion data and the work type;

[0034] The correlation analysis unit is used to determine whether the correlation between the action data and the type of work has reached a preset threshold. If the correlation does not reach the preset threshold, the smart helmet collects the physiological status data of the target construction worker and evaluates the current health index of the target construction worker based on the physiological status data.

[0035] an assistance instruction sending unit, configured to generate an alarm and send it to the monitoring platform if the health index is outside the safety range required for construction, so that the monitoring platform sends an assistance instruction to at least one other construction worker closest to the target construction worker and generates route guidance information to prompt the at least one other construction worker to go to the area where the target construction worker is located;

[0036] The construction area matching unit is used to obtain the historical work data of the target construction personnel if the health index is within the safety range required by the construction, match the target construction personnel with a new construction area based on the historical work data, and dispatch the target construction personnel to the new construction area.

[0037] In a third aspect, the present invention also provides an electronic device comprising a processor and a memory, wherein the memory is used to store computer program code, and the computer program code comprises computer instructions. When the processor executes the computer instructions, the electronic device executes the method as described in the first aspect above and any possible implementation thereof.

[0038] In a fourth aspect, the present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the method as described in the first aspect above and any possible implementation method thereof.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1) This invention collects target construction workers' motion data and geographic location information, and uses this location information to identify the construction worker's location and corresponding work type, enabling precise monitoring of their work status. Furthermore, by analyzing the correlation between the motion data and the work type, if the correlation is high and reaches a preset threshold, it can more accurately determine whether the construction worker is operating in compliance with regulations, thereby improving construction safety management.

[0041] 2) When the correlation between motion data and work type falls below a preset threshold, the present invention uses the smart helmet to collect physiological data from the construction worker to assess their current health index. This process not only quickly identifies potential health risks for construction workers but also generates an immediate alarm and sends it to the monitoring platform if the health index falls outside a safe range. The monitoring platform then sends assistance instructions to the nearest construction workers based on the situation and provides routing information to ensure prompt assistance, significantly shortening emergency response time and reducing the likelihood of accidents.

[0042] 3) Furthermore, if a construction worker's health index is within a safe range, the present invention will leverage their historical work data to match them with a new construction area, rationally allocating human resources and improving work efficiency. In this way, the present invention not only enhances the safety of construction workers but also optimizes the allocation of construction resources, achieving intelligent upgrades in construction management.

[0043] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0045] The accompanying drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to explain the technical solutions of the present disclosure.

[0046] Figure 1 A schematic diagram of a process for a construction worker safety management method using a smart hard hat provided by an embodiment of the present invention;

[0047] Figure 2 A schematic structural diagram of a construction worker safety management and control system for smart safety helmets provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0049] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0050] See also Figure 1 , Figure 1 The following is a flow chart of a method for safety management of construction workers using smart helmets provided by an embodiment of the present invention. Figure 1 As shown, a construction worker safety management method for a smart hard hat includes:

[0051] S10: Collect motion data and geographic location information of target construction personnel, identify the construction area where the target construction personnel are located and the corresponding work type according to the geographic location information, and analyze the correlation between the motion data and the work type.

[0052] The target construction worker refers to the construction worker currently being focused on, and can be any construction worker in the construction site. That is, when a specific construction worker's construction status needs to be analyzed, the worker is regarded as the target construction worker.

[0053] First, the target construction worker's construction data needs to be collected. This is primarily achieved through the use of multiple sensors built into the smart helmet, such as accelerometers and gyroscopes, to collect real-time motion data. These sensors can capture the wearer's head movements, such as tilt angle and rotation speed, to reflect the target construction worker's motion status. In other preferred scenarios, this data can also be obtained through cameras or surveillance video.

[0054] For geographic location information, GPS modules or indoor positioning systems, such as Bluetooth beacons and UWB ultra-wideband technology, are mainly used to obtain the exact location coordinates of construction workers at the construction site.

[0055] Furthermore, based on geographic location information, the construction area where the target construction worker is located can be determined. When arranging construction workers for work, different construction areas are pre-defined in the system based on the construction project plan, and boundary coordinates are set for each area. By comparing the location coordinates obtained from the geographic location information acquisition module, the construction worker's current construction area can be determined. Based on the task allocation records in the construction project management platform, the construction worker's work tasks are associated with the construction area, and the corresponding work type of the construction worker can be identified. RFID tags or other identifiers can also be used to directly associate construction workers with their specific work roles.

[0056] In one embodiment, the method further includes identifying whether the target construction worker is in a dangerous area or a non-construction area based on the geographic location information, and if so, generating an alarm prompt to prompt the target construction worker to go to a safe area or a construction area.

[0057] Typically, if a construction worker is located within a construction zone, their location and type of work can be identified based on their geographic location. However, there are other situations where the worker is located in a dangerous area or a non-construction zone. In these cases, a timely alarm is required to alert the worker to a safe area or construction zone.

[0058] Understandably, construction project planning and safety regulations often predefine detailed site maps, clearly marking various construction areas, prohibited hazardous areas (such as high-voltage areas and areas where heavy machinery is operated), and safe evacuation routes. As the project progresses, the definitions of construction and hazardous areas must be updated promptly to ensure the accuracy and timeliness of this information. This can be achieved through manual input by site managers or automated detection mechanisms.

[0059] When receiving new geographic location information, the system will immediately compare it with the pre-set construction area and danger zone boundaries. If the construction personnel are found to be in the danger zone or have deviated from the designated construction area, an alarm will be triggered. Specifically, the alarm can be as follows:

[0060] Level 1 warning: When construction workers approach but do not enter the danger zone, the smart helmet will emit a slight vibration or sound prompt to remind them of their current location.

[0061] Level 2 warning: Once it is confirmed that construction workers have entered the danger zone or left the construction area, a stronger alarm instruction is immediately generated, including but not limited to a continuous alarm sound, a flashing red LED, and voice prompts to guide construction workers on how to quickly reach the nearest safe area or construction area.

[0062] This approach not only effectively prevents construction workers from straying into dangerous areas or straying from the construction site, but also significantly improves the speed and accuracy of emergency response, reducing the likelihood of accidents. Furthermore, this intelligent location monitoring system helps optimize construction site personnel scheduling, ensuring that every worker is in the correct location and working efficiently, thereby improving the management level and safety of the entire project.

[0063] In one embodiment, after obtaining the work type corresponding to the construction area where the target construction worker is located, in order to analyze whether the target construction worker's operating behavior is standard, it is necessary to further analyze the correlation between the action data and the work type, which specifically includes the following steps:

[0064] Assign work types to construction areas and extract features from motion data, constructing feature vectors based on average acceleration, standard deviation, frequency distribution, and angular velocity change rate;

[0065] The dependency relationship between the job type and each eigenvector is established, and the posterior probability under the given job type condition is calculated through the Bayesian network model. The correlation degree between the action data and the job type is analyzed according to the size of the posterior probability value.

[0066] To facilitate understanding, the following is an example:

[0067] 1) Define the node:

[0068] Work Type Node : Indicates the type of construction worker, such as concrete worker, steel worker, electrician, etc.

[0069] Eigenvector Node : represents different features extracted from motion data, such as average acceleration, standard deviation, frequency distribution, and angular velocity change rate;

[0070] 2) Establish dependencies:

[0071] Each eigenvector node Depends on the work type node , indicating that the eigenvector distributions of different types of work are different. For each eigenvector node , it is necessary to define its Conditional probability distribution under conditions , for the work type node , we need to define its prior probability .

[0072] 3) Calculate the posterior probability:

[0073] According to Bayes' theorem, the given eigenvector can be calculated The posterior probability of the job type under ;

[0074] ;

[0075] Where, Is a node of a given work type Under the condition that The joint conditional probability of is the prior probability of the job type, is the marginal probability of the eigenvector, calculated using the total probability formula;

[0076] Since the eigenvector Contains multiple features , the joint conditional probability can be decomposed into the product of the conditional probabilities of each feature:

[0077] ;

[0078] Assuming conditional independence between features, the above formula is simplified to:

[0079] ;

[0080] Where, Indicates continuous multiplication.

[0081] Therefore, through the above process, the posterior probability under the given work type conditions can be obtained. The degree of correlation between the action data and the work type can be analyzed according to the size of the posterior probability value. The size of the posterior probability can be directly used to represent the size of the correlation.

[0082] In a preferred embodiment, an action pattern library can also be created for each type of work. This library contains the typical action characteristics that construction workers may produce during normal operations under that type of work. Machine learning algorithms such as decision trees, support vector machines, and neural networks can be used to train these models to identify standard action sequences for specific work types. When analyzing the correlation between action data and work types, it is only necessary to compare and analyze the action data collected in real time with the pre-established action pattern library for the corresponding work type to calculate the similarity score between the current action data and the standard action of the work type, that is, the correlation degree.

[0083] S20. Determine whether the correlation between the motion data and the type of work reaches a preset threshold; when the correlation does not reach the preset threshold, collect the physiological status data of the target construction worker through the smart safety helmet, and evaluate the current health index of the target construction worker based on the physiological status data.

[0084] When the correlation does not reach the preset threshold, it means that the current target construction worker's construction status is abnormal, that is, it does not meet the operating specifications required by the construction. Generally speaking, there are several reasons for this phenomenon: one is that the construction worker has decent experience, but due to poor physical condition, the construction status is poor and cannot meet the construction specifications; the other is that the construction worker lacks experience, so the construction ability cannot match the construction requirements; there is also a situation where the construction worker's own construction attitude has problems, and there are behaviors such as laziness, which leads to the construction failing to meet the requirements. In order to further analyze the reasons why the correlation between the construction worker's motion data and the type of work does not reach the preset threshold, this embodiment needs to first collect the physiological status data of the target construction worker through the smart safety helmet, and evaluate the current health index of the target construction worker based on the physiological status data.

[0085] In one embodiment, the physiological status data includes heart rate, heart rate variability, blood pressure, body temperature, and blood oxygen saturation. The smart helmet provided by the present invention generally integrates multiple biosensors that can monitor the wearer's physiological status, including but not limited to:

[0086] Heart rate sensor (HR): used to detect heart rate.

[0087] Blood oxygen saturation sensor (SpO2): Measures the oxygen level in the blood.

[0088] Temperature and humidity sensor: monitors ambient temperature and humidity, as well as skin surface temperature.

[0089] Accelerometer / gyroscope: Although mainly used for motion data collection, it can also help determine the wearer's activity intensity.

[0090] The smart helmet also features built-in Bluetooth, Wi-Fi, or other wireless transmission technologies, ensuring real-time or scheduled upload of physiological data to a central server or cloud platform for processing. A high-efficiency battery and charging solution ensures the power supply required for extended operation. When the system detects a need for further health assessment, the smart helmet begins collecting data points for the aforementioned physiological parameters at a regular frequency, such as once per minute. Digital filters are then used to remove potential noise interference, such as motion artifacts, to improve the quality of the raw data. The data is also standardized to make it suitable for subsequent analysis.

[0091] Furthermore, the current health index of the target construction workers is evaluated based on physiological status data, mainly including:

[0092] 1) Feature extraction: Extract key feature values ​​from the collected physiological data, such as average heart rate, maximum and minimum heart rate difference, average blood oxygen saturation, etc.

[0093] 2) Establish an assessment model: Based on medical knowledge and big data analysis, construct one or more health index assessment models. This is preferably achieved using machine learning methods, such as training classifiers such as support vector machines (SVMs), random forests (RFs), and neural networks to predict health status. Statistical methods can also be used to set thresholds and directly compare actual measured physiological indicators to see if they are within normal ranges.

[0094] 3) Personalized adjustment: Taking into account the differences between different individuals, target construction workers are allowed to enter their personal information (age, gender, medical history, etc.) to more accurately adjust the parameters of the evaluation model and provide personalized health recommendations.

[0095] Once the health index assessment is complete, the results are immediately transmitted back to the smart helmet, which then issues an alert via vibration, sound, or LED lighting to the wearer, reminding them to take a break or take other necessary actions. Remote monitoring and alarm functions are also provided, and health index information is updated simultaneously on the remote monitoring platform for management to review. If an abnormality is detected, the system automatically generates an alarm, notifying the relevant personnel to take appropriate measures, such as dispatching medical assistance.

[0096] Therefore, this embodiment, by combining dual monitoring of motion data and physiological status data, can more accurately identify potential risk situations, reduce the occurrence of false alarms and missed alarms, and thus improve the reliability and effectiveness of the early warning system. Real-time monitoring of construction workers' physiological status and timely response to abnormal situations help prevent accidents caused by overwork or other health problems, protecting the physical health and life safety of construction workers. By automatically determining the correlation between motion data and work types and automatically triggering further health checks when necessary, it simplifies the safety management process and improves the energy efficiency of construction management.

[0097] S30. If the health index is outside the safety range required for construction, an alarm prompt is generated and sent to the monitoring platform, so that the monitoring platform sends an assistance instruction to at least one other construction worker closest to the target construction worker, and generates path guidance information to prompt at least one other construction worker to go to the area where the target construction worker is located.

[0098] If the health index falls outside the safe range required for construction, indicating a physical condition problem for the construction worker, an alarm message will be automatically generated, including the construction worker's identity information, current location coordinates, and preliminary diagnosis results (such as "suspected excessive fatigue" or "abnormal heart rate"). This alarm message is quickly transmitted to the remote monitoring platform via the wireless communication network. To ensure that the target construction worker receives timely assistance, assistance instructions should be sent to other nearby construction workers. In addition, to ensure timely communication of the message, an alarm should be sent to the monitoring platform to promptly notify the medical team to provide assistance. Preferably, on-site management personnel and designated contacts can also be notified via text message, phone call, or other instant messaging tools.

[0099] When generating route guidance information, the map service API can be used, combined with real-time traffic conditions and other obstacle information, to provide rescue workers with the optimal route from their current location to the area where the target construction workers are located. Preferably, real-time updated navigation guidance is also provided to help rescue workers avoid any new obstacles or delays, ensuring that they can reach their destination in the shortest possible time. Throughout the rescue process, the monitoring platform will continuously track the progress of the assistance, ensure the smooth arrival of rescue workers, and provide necessary support. After the incident is over, the system will automatically generate a detailed incident report, recording key details of the entire process for subsequent review and improvement.

[0100] In one embodiment, to ensure the accuracy of path guidance information, the geographic location of the target construction worker to be rescued must be accurately determined. However, due to the complex environment of the construction site, signal transmission is easily affected by surrounding building materials such as concrete, steel bars, and plate materials, resulting in multipath effects. This ultimately renders the positioning information collected by the smart helmet inaccurate, thus affecting the timeliness of rescue efforts. Therefore, this embodiment provides a method that compensates for geographic location information, thereby improving the positioning accuracy of the target construction worker and buying time for rescue.

[0101] Specifically, the correction process is as follows:

[0102] Calculate the The received signal of the path:

[0103] ;

[0104] Where, Indicates the The received signal of the path, Indicates the The amplitude of the received signal on each path, is the imaginary unit, is the carrier frequency, It is The initial phase of each path, Indicates time;

[0105] Determine the The key parameters of each path include amplitude attenuation factor and phase offset:

[0106] ;

[0107] ;

[0108] Where, Indicates the path The amplitude attenuation factor, Indicates the path The geometric distance, Represents the path loss index, satisfying ; Indicates the path The phase offset, is a direct path signal, which represents the shortest path between the transmitting source and the receiving point;

[0109] Determine the first The path quality weight of each path:

[0110] ;

[0111] ;

[0112] ;

[0113] Where, For the The path quality weight of each path, are the path reliability indices before and after normalization, are the minimum and maximum values ​​of the path reliability index, is the adjustment coefficient, satisfying ;

[0114] Get the initial position information collected by the sensor , correct the initial location information and obtain the final geographic location information of the target construction personnel :

[0115] ;

[0116] ;

[0117] Where, Indicates signal transmission sharing Path, Indicates that the signal The propagation speed along the path.

[0118] In this model, by accurately modeling and correcting for multipath effects, the impact of multipath errors on positioning results is reduced, significantly improving positioning accuracy. The introduction of path quality weights enables the system to dynamically adapt to varying environmental conditions, enhancing its robustness and adaptability, particularly in complex and changing construction site environments. Quantitatively assessing the reliability of different paths allows for better allocation of computing resources, saving costs and improving work efficiency. Furthermore, this method completes the entire process from data acquisition to position correction in a short period of time, ensuring the system's real-time responsiveness and making it suitable for applications requiring rapid decision-making.

[0119] S40: If the health index is within the safety range required for construction, obtain the historical work data of the target construction personnel, match a new construction area for the target construction personnel based on the historical work data, and dispatch the target construction personnel to the new construction area.

[0120] In this embodiment, once the health index is determined to be within the safe range required for construction, it indicates that the abnormal working status is not caused by health conditions but by the target construction worker. At this point, the construction worker's historical work records are retrieved from the database. This data includes, but is not limited to: past work areas and types of work, work hours and rest periods, work efficiency and quality evaluations, any special skills or training, and whether the worker has handled similar construction tasks before.

[0121] Furthermore, based on the progress and plan of the current construction project, determine which construction areas require additional manpower support or workers with specific skills. Using intelligent matching algorithms based on rules or machine learning, taking into account factors such as the construction worker's historical work performance, the needs of the current construction area, and personal health status, recommend the most suitable new construction area for each construction worker. For example: If a construction worker has performed well in the past and has relevant experience, he or she will be given priority to tasks that need to be completed efficiently. For more complex or higher-risk tasks, construction workers with corresponding skill certifications are selected. Taking into account each person's unique situation, such as physical recovery status, personal preferences, etc., a certain degree of manual adjustment is allowed to ensure that the final allocation plan is both scientific and humane.

[0122] Once a new work area is identified, the system automatically generates the optimal route from the current location to the new work area and provides detailed navigation instructions to help construction workers reach their destination quickly and accurately. Dispatch instructions, including new work tasks, estimated arrival times, and route information, are sent to construction workers via smart helmets or other mobile devices. Workers are also asked to confirm that they have received and understood the tasks to ensure accurate communication.

[0123] Therefore, through scientific and rational personnel scheduling, this method ensures that each construction area has sufficient and appropriate human resources, avoiding idle or overused manpower and improving overall project efficiency. Accurately matching target construction workers based on their historical work data and personal characteristics not only fully leverages their strengths but also promotes their professional and personal growth. By rationally arranging work tasks while ensuring the health of target construction workers, it reduces safety hazards caused by fatigue or unfamiliarity with the environment, and improves construction site management safety, energy efficiency, and resource utilization.

[0124] In summary, the method provided by the present invention can at least achieve the following beneficial effects:

[0125] 1) This invention collects target construction workers' motion data and geographic location information, and uses this location information to identify the construction worker's location and corresponding work type, enabling precise monitoring of their work status. Furthermore, by analyzing the correlation between the motion data and the work type, if the correlation is high and reaches a preset threshold, it can more accurately determine whether the construction worker is operating in compliance with regulations, thereby improving construction safety management.

[0126] 2) When the correlation between motion data and work type falls below a preset threshold, the present invention uses the smart helmet to collect physiological data from the construction worker to assess their current health index. This process not only quickly identifies potential health risks for construction workers but also generates an immediate alarm and sends it to the monitoring platform if the health index falls outside a safe range. The monitoring platform then sends assistance instructions to the nearest construction workers based on the situation and provides routing information to ensure prompt assistance, significantly shortening emergency response time and reducing the likelihood of accidents.

[0127] 3) Furthermore, if a construction worker's health index is within a safe range, the present invention will leverage their historical work data to match them with a new construction area, rationally allocating human resources and improving work efficiency. In this way, the present invention not only enhances the safety of construction workers but also optimizes the allocation of construction resources, achieving intelligent upgrades in construction management.

[0128] See also Figure 2 In one embodiment, the present invention further provides a construction worker safety management and control system for a smart hard hat, the system comprising:

[0129] The data collection unit 100 is used to collect the motion data and geographic location information of the target construction personnel, identify the construction area and corresponding work type of the target construction personnel based on the geographic location information, and analyze the correlation between the motion data and the work type;

[0130] The correlation analysis unit 200 is used to determine whether the correlation between the action data and the type of work has reached a preset threshold; if the correlation has not reached the preset threshold, the smart helmet is used to collect physiological status data of the target construction worker and the current health index of the target construction worker is evaluated based on the physiological status data;

[0131] The assistance instruction sending unit 300 is configured to generate an alarm and send it to the monitoring platform if the health index is outside the safety range required for construction, so that the monitoring platform sends an assistance instruction to at least one other construction worker closest to the target construction worker and generates route guidance information to prompt the at least one other construction worker to go to the area where the target construction worker is located;

[0132] The construction area matching unit 400 is used to obtain the historical work data of the target construction personnel if the health index is within the safety range required by the construction, match a new construction area for the target construction personnel based on the historical work data, and dispatch the target construction personnel to the new construction area.

[0133] It can be understood that the functions or modules included in the system provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0134] The present invention also provides an electronic device, including a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any one of the possible implementation modes.

[0135] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any one of the possible implementation methods described above.

[0136] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0137] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here. Those skilled in the art will also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in one embodiment, reference can be made to the descriptions of other embodiments.

Claims

1. A construction worker safety management method for a smart hard hat, characterized in that: The method comprises: Collect the target construction worker's motion data and geographic location information, identify the target construction worker's construction area and corresponding work type based on the geographic location information, and analyze the correlation between the motion data and the work type; Collecting the geographic location information of the target construction personnel includes: Calculate the The received signal of the path: ； Where, Indicates the The received signal of the path, Indicates the The amplitude of the received signal on each path, is the imaginary unit, is the carrier frequency, It is The initial phase of each path, Indicates time; Determine the The key parameters of each path include amplitude attenuation factor and phase offset: ； ； Where, Indicates the path The amplitude attenuation factor, Indicates the path The geometric distance, Represents the path loss index, satisfying ; Indicates the path The phase offset, is a direct path signal, which represents the shortest path between the transmitting source and the receiving point; Determine the first The path quality weight of each path: ； ； ； Where, For the The path quality weight of each path, are the path reliability indices before and after normalization, are the minimum and maximum values ​​of the path reliability index, is the adjustment coefficient, satisfying ; Get the initial position information collected by the sensor , correct the initial location information and obtain the final geographic location information of the target construction personnel : ； ； Where, Indicates signal transmission sharing Path, Indicates that the signal The propagation speed along the path; Determine whether the correlation between the motion data and the type of work has reached a preset threshold; if the correlation has not reached the preset threshold, collect the physiological status data of the target construction worker through the smart safety helmet, and evaluate the current health index of the target construction worker based on the physiological status data; If the health index is outside the safety range required for construction, an alarm is generated and sent to the monitoring platform, so that the monitoring platform sends an assistance instruction to at least one other construction worker closest to the target construction worker and generates a route guidance message to prompt at least one other construction worker to go to the area where the target construction worker is located; If the health index is within the safety range required by the construction, the historical work data of the target construction personnel is obtained, a new construction area is matched for the target construction personnel based on the historical work data, and the target construction personnel is dispatched to the new construction area.

2. The construction worker safety management and control method for smart safety helmets according to claim 1, characterized in that: The analysis of the correlation between the action data and the type of work includes: Assign work types to construction areas and extract features from motion data, constructing feature vectors based on average acceleration, standard deviation, frequency distribution, and angular velocity change rate; The dependency relationship between the job type and each eigenvector is established, and the posterior probability under the given job type condition is calculated through the Bayesian network model. The correlation degree between the action data and the job type is analyzed according to the size of the posterior probability value.

3. The construction worker safety management and control method for smart helmets according to claim 1, characterized in that: The method further comprises: The target construction personnel are identified based on their geographic location information to determine whether they are in a dangerous area or a non-construction area. If so, an alarm is generated to prompt the target construction personnel to move to a safe area or a construction area.

4. The construction worker safety management and control method for smart helmets according to claim 1, characterized in that: The physiological status data includes heart rate, heart rate variability, blood pressure, body temperature and blood oxygen saturation.

5. A construction worker safety management and control system for smart safety helmets, characterized in that: The system comprises: The data collection unit is used to collect the motion data and geographic location information of the target construction personnel, identify the construction area and corresponding work type of the target construction personnel based on the geographic location information, and analyze the correlation between the motion data and the work type; the geographic location information of the target construction personnel is collected, including: Calculate the The received signal of the path: ； Where, Indicates the The received signal of the path, Indicates the The amplitude of the received signal on each path, is the imaginary unit, is the carrier frequency, It is The initial phase of each path, Indicates time; Determine the The key parameters of each path include amplitude attenuation factor and phase offset: ； ； Where, Indicates the path The amplitude attenuation factor, Indicates the path The geometric distance, Represents the path loss index, satisfying ; Indicates the path The phase offset, is a direct path signal, which represents the shortest path between the transmitting source and the receiving point; Determine the first The path quality weight of each path: ； ； ； Where, For the The path quality weight of each path, are the path reliability indices before and after normalization, are the minimum and maximum values ​​of the path reliability index, is the adjustment coefficient, satisfying ; Get the initial position information collected by the sensor , correct the initial location information and obtain the final geographic location information of the target construction personnel : ； ； Where, Indicates signal transmission sharing Path, Indicates that the signal The propagation speed along the path; The correlation analysis unit is used to determine whether the correlation between the action data and the type of work has reached a preset threshold. If the correlation does not reach the preset threshold, the smart helmet collects the physiological status data of the target construction worker and evaluates the current health index of the target construction worker based on the physiological status data. an assistance instruction sending unit, configured to generate an alarm and send it to the monitoring platform if the health index is outside the safety range required for construction, so that the monitoring platform sends an assistance instruction to at least one other construction worker closest to the target construction worker and generates route guidance information to prompt the at least one other construction worker to go to the area where the target construction worker is located; The construction area matching unit is used to obtain the historical work data of the target construction personnel if the health index is within the safety range required by the construction, match the target construction personnel with a new construction area based on the historical work data, and dispatch the target construction personnel to the new construction area.

6. An electronic device, characterized in that: include: A processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, and when the processor executes the computer instructions, the electronic device executes the construction worker safety management method for a smart safety helmet as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the construction worker safety management method for smart safety helmets as described in any one of claims 1 to 4.

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